#ASSIGNMENT-4
#21BCE9614(VIT AP)
#GOJJA PAVAN KRISHNA
#MORNING BATCH

Import the Libraries

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

Import the Dataset

df=pd.read_csv("WA_Fn-UseC_-HR-Employee-Attrition.csv")

df.head()

	Age	Attrition	BusinessTravel	DailyRate	Department	DistanceFromHome	Education	EducationField	EmployeeCount	EmployeeNumber	...	RelationshipSatisfaction	StandardHours	StockOptionLevel	TotalWorkingYears	TrainingTimesLastYear	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsSinceLastPromotion	YearsWithCurrManager
0	41	Yes	Travel_Rarely	1102	Sales	1	2	Life Sciences	1	1	...	1	80	0	8	0	1	6	4	0	5
1	49	No	Travel_Frequently	279	Research & Development	8	1	Life Sciences	1	2	...	4	80	1	10	3	3	10	7	1	7
2	37	Yes	Travel_Rarely	1373	Research & Development	2	2	Other	1	4	...	2	80	0	7	3	3	0	0	0	0
3	33	No	Travel_Frequently	1392	Research & Development	3	4	Life Sciences	1	5	...	3	80	0	8	3	3	8	7	3	0
4	27	No	Travel_Rarely	591	Research & Development	2	1	Medical	1	7	...	4	80	1	6	3	3	2	2	2	2

5 rows × 35 columns

df.shape

(1470, 35)

df.size

51450

df.DailyRate.value_counts()

691     6
408     5
530     5
1329    5
1082    5
       ..
650     1
279     1
316     1
314     1
628     1
Name: DailyRate, Length: 886, dtype: int64

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1470 entries, 0 to 1469
Data columns (total 35 columns):
 #   Column                    Non-Null Count  Dtype 
---  ------                    --------------  ----- 
 0   Age                       1470 non-null   int64 
 1   Attrition                 1470 non-null   object
 2   BusinessTravel            1470 non-null   object
 3   DailyRate                 1470 non-null   int64 
 4   Department                1470 non-null   object
 5   DistanceFromHome          1470 non-null   int64 
 6   Education                 1470 non-null   int64 
 7   EducationField            1470 non-null   object
 8   EmployeeCount             1470 non-null   int64 
 9   EmployeeNumber            1470 non-null   int64 
 10  EnvironmentSatisfaction   1470 non-null   int64 
 11  Gender                    1470 non-null   object
 12  HourlyRate                1470 non-null   int64 
 13  JobInvolvement            1470 non-null   int64 
 14  JobLevel                  1470 non-null   int64 
 15  JobRole                   1470 non-null   object
 16  JobSatisfaction           1470 non-null   int64 
 17  MaritalStatus             1470 non-null   object
 18  MonthlyIncome             1470 non-null   int64 
 19  MonthlyRate               1470 non-null   int64 
 20  NumCompaniesWorked        1470 non-null   int64 
 21  Over18                    1470 non-null   object
 22  OverTime                  1470 non-null   object
 23  PercentSalaryHike         1470 non-null   int64 
 24  PerformanceRating         1470 non-null   int64 
 25  RelationshipSatisfaction  1470 non-null   int64 
 26  StandardHours             1470 non-null   int64 
 27  StockOptionLevel          1470 non-null   int64 
 28  TotalWorkingYears         1470 non-null   int64 
 29  TrainingTimesLastYear     1470 non-null   int64 
 30  WorkLifeBalance           1470 non-null   int64 
 31  YearsAtCompany            1470 non-null   int64 
 32  YearsInCurrentRole        1470 non-null   int64 
 33  YearsSinceLastPromotion   1470 non-null   int64 
 34  YearsWithCurrManager      1470 non-null   int64 
dtypes: int64(26), object(9)
memory usage: 402.1+ KB

df.describe()

	Age	DailyRate	DistanceFromHome	Education	EmployeeCount	EmployeeNumber	EnvironmentSatisfaction	HourlyRate	JobInvolvement	JobLevel	...	RelationshipSatisfaction	StandardHours	StockOptionLevel	TotalWorkingYears	TrainingTimesLastYear	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsSinceLastPromotion	YearsWithCurrManager
count	1470.000000	1470.000000	1470.000000	1470.000000	1470.0	1470.000000	1470.000000	1470.000000	1470.000000	1470.000000	...	1470.000000	1470.0	1470.000000	1470.000000	1470.000000	1470.000000	1470.000000	1470.000000	1470.000000	1470.000000
mean	36.923810	802.485714	9.192517	2.912925	1.0	1024.865306	2.721769	65.891156	2.729932	2.063946	...	2.712245	80.0	0.793878	11.279592	2.799320	2.761224	7.008163	4.229252	2.187755	4.123129
std	9.135373	403.509100	8.106864	1.024165	0.0	602.024335	1.093082	20.329428	0.711561	1.106940	...	1.081209	0.0	0.852077	7.780782	1.289271	0.706476	6.126525	3.623137	3.222430	3.568136
min	18.000000	102.000000	1.000000	1.000000	1.0	1.000000	1.000000	30.000000	1.000000	1.000000	...	1.000000	80.0	0.000000	0.000000	0.000000	1.000000	0.000000	0.000000	0.000000	0.000000
25%	30.000000	465.000000	2.000000	2.000000	1.0	491.250000	2.000000	48.000000	2.000000	1.000000	...	2.000000	80.0	0.000000	6.000000	2.000000	2.000000	3.000000	2.000000	0.000000	2.000000
50%	36.000000	802.000000	7.000000	3.000000	1.0	1020.500000	3.000000	66.000000	3.000000	2.000000	...	3.000000	80.0	1.000000	10.000000	3.000000	3.000000	5.000000	3.000000	1.000000	3.000000
75%	43.000000	1157.000000	14.000000	4.000000	1.0	1555.750000	4.000000	83.750000	3.000000	3.000000	...	4.000000	80.0	1.000000	15.000000	3.000000	3.000000	9.000000	7.000000	3.000000	7.000000
max	60.000000	1499.000000	29.000000	5.000000	1.0	2068.000000	4.000000	100.000000	4.000000	5.000000	...	4.000000	80.0	3.000000	40.000000	6.000000	4.000000	40.000000	18.000000	15.000000	17.000000

8 rows × 26 columns

#Dropping unwanted columns
df=df.drop(columns=['JobSatisfaction','JobInvolvement','Over18','RelationshipSatisfaction','EnvironmentSatisfaction','DistanceFromHome','StockOptionLevel'],axis=0)

df

	Age	Attrition	BusinessTravel	DailyRate	Department	Education	EducationField	EmployeeCount	EmployeeNumber	Gender	...	PercentSalaryHike	PerformanceRating	StandardHours	TotalWorkingYears	TrainingTimesLastYear	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsSinceLastPromotion	YearsWithCurrManager
0	41	Yes	Travel_Rarely	1102	Sales	2	Life Sciences	1	1	Female	...	11	3	80	8	0	1	6	4	0	5
1	49	No	Travel_Frequently	279	Research & Development	1	Life Sciences	1	2	Male	...	23	4	80	10	3	3	10	7	1	7
2	37	Yes	Travel_Rarely	1373	Research & Development	2	Other	1	4	Male	...	15	3	80	7	3	3	0	0	0	0
3	33	No	Travel_Frequently	1392	Research & Development	4	Life Sciences	1	5	Female	...	11	3	80	8	3	3	8	7	3	0
4	27	No	Travel_Rarely	591	Research & Development	1	Medical	1	7	Male	...	12	3	80	6	3	3	2	2	2	2
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1465	36	No	Travel_Frequently	884	Research & Development	2	Medical	1	2061	Male	...	17	3	80	17	3	3	5	2	0	3
1466	39	No	Travel_Rarely	613	Research & Development	1	Medical	1	2062	Male	...	15	3	80	9	5	3	7	7	1	7
1467	27	No	Travel_Rarely	155	Research & Development	3	Life Sciences	1	2064	Male	...	20	4	80	6	0	3	6	2	0	3
1468	49	No	Travel_Frequently	1023	Sales	3	Medical	1	2065	Male	...	14	3	80	17	3	2	9	6	0	8
1469	34	No	Travel_Rarely	628	Research & Development	3	Medical	1	2068	Male	...	12	3	80	6	3	4	4	3	1	2

1470 rows × 28 columns

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1470 entries, 0 to 1469
Data columns (total 28 columns):
 #   Column                   Non-Null Count  Dtype 
---  ------                   --------------  ----- 
 0   Age                      1470 non-null   int64 
 1   Attrition                1470 non-null   object
 2   BusinessTravel           1470 non-null   object
 3   DailyRate                1470 non-null   int64 
 4   Department               1470 non-null   object
 5   Education                1470 non-null   int64 
 6   EducationField           1470 non-null   object
 7   EmployeeCount            1470 non-null   int64 
 8   EmployeeNumber           1470 non-null   int64 
 9   Gender                   1470 non-null   object
 10  HourlyRate               1470 non-null   int64 
 11  JobLevel                 1470 non-null   int64 
 12  JobRole                  1470 non-null   object
 13  MaritalStatus            1470 non-null   object
 14  MonthlyIncome            1470 non-null   int64 
 15  MonthlyRate              1470 non-null   int64 
 16  NumCompaniesWorked       1470 non-null   int64 
 17  OverTime                 1470 non-null   object
 18  PercentSalaryHike        1470 non-null   int64 
 19  PerformanceRating        1470 non-null   int64 
 20  StandardHours            1470 non-null   int64 
 21  TotalWorkingYears        1470 non-null   int64 
 22  TrainingTimesLastYear    1470 non-null   int64 
 23  WorkLifeBalance          1470 non-null   int64 
 24  YearsAtCompany           1470 non-null   int64 
 25  YearsInCurrentRole       1470 non-null   int64 
 26  YearsSinceLastPromotion  1470 non-null   int64 
 27  YearsWithCurrManager     1470 non-null   int64 
dtypes: int64(20), object(8)
memory usage: 321.7+ KB

Checking for null values

df.isnull().any()

Age                        False
Attrition                  False
BusinessTravel             False
DailyRate                  False
Department                 False
Education                  False
EducationField             False
EmployeeCount              False
EmployeeNumber             False
Gender                     False
HourlyRate                 False
JobLevel                   False
JobRole                    False
MaritalStatus              False
MonthlyIncome              False
MonthlyRate                False
NumCompaniesWorked         False
OverTime                   False
PercentSalaryHike          False
PerformanceRating          False
StandardHours              False
TotalWorkingYears          False
TrainingTimesLastYear      False
WorkLifeBalance            False
YearsAtCompany             False
YearsInCurrentRole         False
YearsSinceLastPromotion    False
YearsWithCurrManager       False
dtype: bool

df.isnull().sum()

Age                        0
Attrition                  0
BusinessTravel             0
DailyRate                  0
Department                 0
Education                  0
EducationField             0
EmployeeCount              0
EmployeeNumber             0
Gender                     0
HourlyRate                 0
JobLevel                   0
JobRole                    0
MaritalStatus              0
MonthlyIncome              0
MonthlyRate                0
NumCompaniesWorked         0
OverTime                   0
PercentSalaryHike          0
PerformanceRating          0
StandardHours              0
TotalWorkingYears          0
TrainingTimesLastYear      0
WorkLifeBalance            0
YearsAtCompany             0
YearsInCurrentRole         0
YearsSinceLastPromotion    0
YearsWithCurrManager       0
dtype: int64

Data Vizualization

sns.distplot(df["Age"])

C:\ProgramData\Anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)

<AxesSubplot:xlabel='Age', ylabel='Density'>

plt.scatter(df["Age"],df["DailyRate"])

<matplotlib.collections.PathCollection at 0x215ddaabdc0>

sns.lineplot(x='DailyRate',y='Age',data=df)

<AxesSubplot:xlabel='DailyRate', ylabel='Age'>

df.corr()

	Age	DailyRate	Education	EmployeeCount	EmployeeNumber	HourlyRate	JobLevel	MonthlyIncome	MonthlyRate	NumCompaniesWorked	PercentSalaryHike	PerformanceRating	StandardHours	TotalWorkingYears	TrainingTimesLastYear	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsSinceLastPromotion	YearsWithCurrManager
Age	1.000000	0.010661	0.208034	NaN	-0.010145	0.024287	0.509604	0.497855	0.028051	0.299635	0.003634	0.001904	NaN	0.680381	-0.019621	-0.021490	0.311309	0.212901	0.216513	0.202089
DailyRate	0.010661	1.000000	-0.016806	NaN	-0.050990	0.023381	0.002966	0.007707	-0.032182	0.038153	0.022704	0.000473	NaN	0.014515	0.002453	-0.037848	-0.034055	0.009932	-0.033229	-0.026363
Education	0.208034	-0.016806	1.000000	NaN	0.042070	0.016775	0.101589	0.094961	-0.026084	0.126317	-0.011111	-0.024539	NaN	0.148280	-0.025100	0.009819	0.069114	0.060236	0.054254	0.069065
EmployeeCount	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
EmployeeNumber	-0.010145	-0.050990	0.042070	NaN	1.000000	0.035179	-0.018519	-0.014829	0.012648	-0.001251	-0.012944	-0.020359	NaN	-0.014365	0.023603	0.010309	-0.011240	-0.008416	-0.009019	-0.009197
HourlyRate	0.024287	0.023381	0.016775	NaN	0.035179	1.000000	-0.027853	-0.015794	-0.015297	0.022157	-0.009062	-0.002172	NaN	-0.002334	-0.008548	-0.004607	-0.019582	-0.024106	-0.026716	-0.020123
JobLevel	0.509604	0.002966	0.101589	NaN	-0.018519	-0.027853	1.000000	0.950300	0.039563	0.142501	-0.034730	-0.021222	NaN	0.782208	-0.018191	0.037818	0.534739	0.389447	0.353885	0.375281
MonthlyIncome	0.497855	0.007707	0.094961	NaN	-0.014829	-0.015794	0.950300	1.000000	0.034814	0.149515	-0.027269	-0.017120	NaN	0.772893	-0.021736	0.030683	0.514285	0.363818	0.344978	0.344079
MonthlyRate	0.028051	-0.032182	-0.026084	NaN	0.012648	-0.015297	0.039563	0.034814	1.000000	0.017521	-0.006429	-0.009811	NaN	0.026442	0.001467	0.007963	-0.023655	-0.012815	0.001567	-0.036746
NumCompaniesWorked	0.299635	0.038153	0.126317	NaN	-0.001251	0.022157	0.142501	0.149515	0.017521	1.000000	-0.010238	-0.014095	NaN	0.237639	-0.066054	-0.008366	-0.118421	-0.090754	-0.036814	-0.110319
PercentSalaryHike	0.003634	0.022704	-0.011111	NaN	-0.012944	-0.009062	-0.034730	-0.027269	-0.006429	-0.010238	1.000000	0.773550	NaN	-0.020608	-0.005221	-0.003280	-0.035991	-0.001520	-0.022154	-0.011985
PerformanceRating	0.001904	0.000473	-0.024539	NaN	-0.020359	-0.002172	-0.021222	-0.017120	-0.009811	-0.014095	0.773550	1.000000	NaN	0.006744	-0.015579	0.002572	0.003435	0.034986	0.017896	0.022827
StandardHours	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
TotalWorkingYears	0.680381	0.014515	0.148280	NaN	-0.014365	-0.002334	0.782208	0.772893	0.026442	0.237639	-0.020608	0.006744	NaN	1.000000	-0.035662	0.001008	0.628133	0.460365	0.404858	0.459188
TrainingTimesLastYear	-0.019621	0.002453	-0.025100	NaN	0.023603	-0.008548	-0.018191	-0.021736	0.001467	-0.066054	-0.005221	-0.015579	NaN	-0.035662	1.000000	0.028072	0.003569	-0.005738	-0.002067	-0.004096
WorkLifeBalance	-0.021490	-0.037848	0.009819	NaN	0.010309	-0.004607	0.037818	0.030683	0.007963	-0.008366	-0.003280	0.002572	NaN	0.001008	0.028072	1.000000	0.012089	0.049856	0.008941	0.002759
YearsAtCompany	0.311309	-0.034055	0.069114	NaN	-0.011240	-0.019582	0.534739	0.514285	-0.023655	-0.118421	-0.035991	0.003435	NaN	0.628133	0.003569	0.012089	1.000000	0.758754	0.618409	0.769212
YearsInCurrentRole	0.212901	0.009932	0.060236	NaN	-0.008416	-0.024106	0.389447	0.363818	-0.012815	-0.090754	-0.001520	0.034986	NaN	0.460365	-0.005738	0.049856	0.758754	1.000000	0.548056	0.714365
YearsSinceLastPromotion	0.216513	-0.033229	0.054254	NaN	-0.009019	-0.026716	0.353885	0.344978	0.001567	-0.036814	-0.022154	0.017896	NaN	0.404858	-0.002067	0.008941	0.618409	0.548056	1.000000	0.510224
YearsWithCurrManager	0.202089	-0.026363	0.069065	NaN	-0.009197	-0.020123	0.375281	0.344079	-0.036746	-0.110319	-0.011985	0.022827	NaN	0.459188	-0.004096	0.002759	0.769212	0.714365	0.510224	1.000000

df.head()

	Age	Attrition	BusinessTravel	DailyRate	Department	Education	EducationField	EmployeeCount	EmployeeNumber	Gender	...	PercentSalaryHike	PerformanceRating	StandardHours	TotalWorkingYears	TrainingTimesLastYear	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsSinceLastPromotion	YearsWithCurrManager
0	41	Yes	Travel_Rarely	1102	Sales	2	Life Sciences	1	1	Female	...	11	3	80	8	0	1	6	4	0	5
1	49	No	Travel_Frequently	279	Research & Development	1	Life Sciences	1	2	Male	...	23	4	80	10	3	3	10	7	1	7
2	37	Yes	Travel_Rarely	1373	Research & Development	2	Other	1	4	Male	...	15	3	80	7	3	3	0	0	0	0
3	33	No	Travel_Frequently	1392	Research & Development	4	Life Sciences	1	5	Female	...	11	3	80	8	3	3	8	7	3	0
4	27	No	Travel_Rarely	591	Research & Development	1	Medical	1	7	Male	...	12	3	80	6	3	3	2	2	2	2

5 rows × 28 columns

sns.heatmap(df.corr(),annot=True)

<AxesSubplot:>

sns.pairplot(df)

<seaborn.axisgrid.PairGrid at 0x21792a0c490>

sns.barplot(x=df["Age"],y=df["DailyRate"],ci=0)

<AxesSubplot:xlabel='Age', ylabel='DailyRate'>

Outlier Detection

sns.boxplot(df.TotalWorkingYears)

C:\ProgramData\Anaconda3\lib\site-packages\seaborn\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(

<AxesSubplot:xlabel='TotalWorkingYears'>

sns.boxplot(df.Age)

C:\ProgramData\Anaconda3\lib\site-packages\seaborn\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(

<AxesSubplot:xlabel='Age'>

sns.boxplot(df.YearsAtCompany)

C:\ProgramData\Anaconda3\lib\site-packages\seaborn\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(

<AxesSubplot:xlabel='YearsAtCompany'>

q1=df.YearsAtCompany.quantile(0.25)
q3=df.YearsAtCompany.quantile(0.75)
q1,q3

(3.0, 9.0)

IQR=q3-q1

upper_limit=q3+1.5*IQR
upper_limit

18.0

df=df[df['YearsAtCompany']<upper_limit]
sns.boxplot(df['YearsAtCompany'])

C:\ProgramData\Anaconda3\lib\site-packages\seaborn\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(

<AxesSubplot:xlabel='YearsAtCompany'>

Splitting Dependent and Independent variables

df

	Age	Attrition	BusinessTravel	DailyRate	Department	Education	EducationField	EmployeeCount	EmployeeNumber	Gender	...	PercentSalaryHike	PerformanceRating	StandardHours	TotalWorkingYears	TrainingTimesLastYear	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsSinceLastPromotion	YearsWithCurrManager
0	41	Yes	Travel_Rarely	1102	Sales	2	Life Sciences	1	1	Female	...	11	3	80	8	0	1	6	4	0	5
1	49	No	Travel_Frequently	279	Research & Development	1	Life Sciences	1	2	Male	...	23	4	80	10	3	3	10	7	1	7
2	37	Yes	Travel_Rarely	1373	Research & Development	2	Other	1	4	Male	...	15	3	80	7	3	3	0	0	0	0
3	33	No	Travel_Frequently	1392	Research & Development	4	Life Sciences	1	5	Female	...	11	3	80	8	3	3	8	7	3	0
4	27	No	Travel_Rarely	591	Research & Development	1	Medical	1	7	Male	...	12	3	80	6	3	3	2	2	2	2
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1465	36	No	Travel_Frequently	884	Research & Development	2	Medical	1	2061	Male	...	17	3	80	17	3	3	5	2	0	3
1466	39	No	Travel_Rarely	613	Research & Development	1	Medical	1	2062	Male	...	15	3	80	9	5	3	7	7	1	7
1467	27	No	Travel_Rarely	155	Research & Development	3	Life Sciences	1	2064	Male	...	20	4	80	6	0	3	6	2	0	3
1468	49	No	Travel_Frequently	1023	Sales	3	Medical	1	2065	Male	...	14	3	80	17	3	2	9	6	0	8
1469	34	No	Travel_Rarely	628	Research & Development	3	Medical	1	2068	Male	...	12	3	80	6	3	4	4	3	1	2

1470 rows × 28 columns

X=df.drop(columns=['Attrition'])
X.head()

	Age	BusinessTravel	DailyRate	Department	Education	EducationField	EmployeeCount	EmployeeNumber	Gender	HourlyRate	...	PercentSalaryHike	PerformanceRating	StandardHours	TotalWorkingYears	TrainingTimesLastYear	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsSinceLastPromotion	YearsWithCurrManager
0	41	Travel_Rarely	1102	Sales	2	Life Sciences	1	1	Female	94	...	11	3	80	8	0	1	6	4	0	5
1	49	Travel_Frequently	279	Research & Development	1	Life Sciences	1	2	Male	61	...	23	4	80	10	3	3	10	7	1	7
2	37	Travel_Rarely	1373	Research & Development	2	Other	1	4	Male	92	...	15	3	80	7	3	3	0	0	0	0
3	33	Travel_Frequently	1392	Research & Development	4	Life Sciences	1	5	Female	56	...	11	3	80	8	3	3	8	7	3	0
4	27	Travel_Rarely	591	Research & Development	1	Medical	1	7	Male	40	...	12	3	80	6	3	3	2	2	2	2

5 rows × 27 columns

y=df.Attrition
y.head()

0    Yes
1     No
2    Yes
3     No
4     No
Name: Attrition, dtype: object

Label Encoding

from sklearn.preprocessing import LabelEncoder
le=LabelEncoder()
X.BusinessTravel=le.fit_transform(X.BusinessTravel)
X.Department=le.fit_transform(X.Department)
X.EducationField=le.fit_transform(X.EducationField)
X.Gender=le.fit_transform(X.Gender)
X.JobRole=le.fit_transform(X.JobRole)
X.MaritalStatus=le.fit_transform(X.MaritalStatus)
X.OverTime=le.fit_transform(X.OverTime)
X.head()

	Age	BusinessTravel	DailyRate	Department	Education	EducationField	EmployeeCount	EmployeeNumber	Gender	HourlyRate	...	PercentSalaryHike	PerformanceRating	StandardHours	TotalWorkingYears	TrainingTimesLastYear	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsSinceLastPromotion	YearsWithCurrManager
0	41	2	1102	2	2	1	1	1	0	94	...	11	3	80	8	0	1	6	4	0	5
1	49	1	279	1	1	1	1	2	1	61	...	23	4	80	10	3	3	10	7	1	7
2	37	2	1373	1	2	4	1	4	1	92	...	15	3	80	7	3	3	0	0	0	0
3	33	1	1392	1	4	1	1	5	0	56	...	11	3	80	8	3	3	8	7	3	0
4	27	2	591	1	1	3	1	7	1	40	...	12	3	80	6	3	3	2	2	2	2

5 rows × 27 columns

X.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1470 entries, 0 to 1469
Data columns (total 27 columns):
 #   Column                   Non-Null Count  Dtype
---  ------                   --------------  -----
 0   Age                      1470 non-null   int64
 1   BusinessTravel           1470 non-null   int64
 2   DailyRate                1470 non-null   int64
 3   Department               1470 non-null   int64
 4   Education                1470 non-null   int64
 5   EducationField           1470 non-null   int64
 6   EmployeeCount            1470 non-null   int64
 7   EmployeeNumber           1470 non-null   int64
 8   Gender                   1470 non-null   int64
 9   HourlyRate               1470 non-null   int64
 10  JobLevel                 1470 non-null   int64
 11  JobRole                  1470 non-null   int64
 12  MaritalStatus            1470 non-null   int64
 13  MonthlyIncome            1470 non-null   int64
 14  MonthlyRate              1470 non-null   int64
 15  NumCompaniesWorked       1470 non-null   int64
 16  OverTime                 1470 non-null   int32
 17  PercentSalaryHike        1470 non-null   int64
 18  PerformanceRating        1470 non-null   int64
 19  StandardHours            1470 non-null   int64
 20  TotalWorkingYears        1470 non-null   int64
 21  TrainingTimesLastYear    1470 non-null   int64
 22  WorkLifeBalance          1470 non-null   int64
 23  YearsAtCompany           1470 non-null   int64
 24  YearsInCurrentRole       1470 non-null   int64
 25  YearsSinceLastPromotion  1470 non-null   int64
 26  YearsWithCurrManager     1470 non-null   int64
dtypes: int32(1), int64(26)
memory usage: 304.5 KB

Feature Scaling

from sklearn.preprocessing import MinMaxScaler
ms=MinMaxScaler()

X_Scaled=pd.DataFrame(ms.fit_transform(X),columns=X.columns)

X_Scaled

	Age	BusinessTravel	DailyRate	Department	Education	EducationField	EmployeeCount	EmployeeNumber	Gender	HourlyRate	...	PercentSalaryHike	PerformanceRating	StandardHours	TotalWorkingYears	TrainingTimesLastYear	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsSinceLastPromotion	YearsWithCurrManager
0	0.547619	1.0	0.715820	1.0	0.25	0.2	0.0	0.000000	0.0	0.914286	...	0.000000	0.0	0.0	0.200	0.000000	0.000000	0.150	0.222222	0.000000	0.294118
1	0.738095	0.5	0.126700	0.5	0.00	0.2	0.0	0.000484	1.0	0.442857	...	0.857143	1.0	0.0	0.250	0.500000	0.666667	0.250	0.388889	0.066667	0.411765
2	0.452381	1.0	0.909807	0.5	0.25	0.8	0.0	0.001451	1.0	0.885714	...	0.285714	0.0	0.0	0.175	0.500000	0.666667	0.000	0.000000	0.000000	0.000000
3	0.357143	0.5	0.923407	0.5	0.75	0.2	0.0	0.001935	0.0	0.371429	...	0.000000	0.0	0.0	0.200	0.500000	0.666667	0.200	0.388889	0.200000	0.000000
4	0.214286	1.0	0.350036	0.5	0.00	0.6	0.0	0.002903	1.0	0.142857	...	0.071429	0.0	0.0	0.150	0.500000	0.666667	0.050	0.111111	0.133333	0.117647
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1465	0.428571	0.5	0.559771	0.5	0.25	0.6	0.0	0.996613	1.0	0.157143	...	0.428571	0.0	0.0	0.425	0.500000	0.666667	0.125	0.111111	0.000000	0.176471
1466	0.500000	1.0	0.365784	0.5	0.00	0.6	0.0	0.997097	1.0	0.171429	...	0.285714	0.0	0.0	0.225	0.833333	0.666667	0.175	0.388889	0.066667	0.411765
1467	0.214286	1.0	0.037938	0.5	0.50	0.2	0.0	0.998065	1.0	0.814286	...	0.642857	1.0	0.0	0.150	0.000000	0.666667	0.150	0.111111	0.000000	0.176471
1468	0.738095	0.5	0.659270	1.0	0.50	0.6	0.0	0.998549	1.0	0.471429	...	0.214286	0.0	0.0	0.425	0.500000	0.333333	0.225	0.333333	0.000000	0.470588
1469	0.380952	1.0	0.376521	0.5	0.50	0.6	0.0	1.000000	1.0	0.742857	...	0.071429	0.0	0.0	0.150	0.500000	1.000000	0.100	0.166667	0.066667	0.117647

1470 rows × 27 columns

Splitting Data into Train and Test

from sklearn.model_selection import train_test_split
x_train,x_test,y_train,y_test=train_test_split(X_Scaled,y,test_size=0.2,random_state=0)

x_train.shape,x_test.shape,y_train.shape,y_test.shape

((1176, 27), (294, 27), (1176,), (294,))

x_train.head()

	Age	BusinessTravel	DailyRate	Department	Education	EducationField	EmployeeCount	EmployeeNumber	Gender	HourlyRate	...	PercentSalaryHike	PerformanceRating	StandardHours	TotalWorkingYears	TrainingTimesLastYear	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsSinceLastPromotion	YearsWithCurrManager
1374	0.952381	1.0	0.360057	1.0	0.50	0.2	0.0	0.937107	0.0	0.600000	...	0.142857	0.0	0.0	0.725	0.333333	0.333333	0.025	0.000000	0.000000	0.000000
1092	0.642857	1.0	0.607015	0.5	0.50	1.0	0.0	0.747460	1.0	0.957143	...	0.642857	1.0	0.0	0.200	0.500000	0.666667	0.125	0.222222	0.000000	0.176471
768	0.523810	1.0	0.141732	1.0	0.50	0.4	0.0	0.515239	1.0	0.628571	...	0.214286	0.0	0.0	0.200	0.500000	0.333333	0.175	0.388889	0.466667	0.294118
569	0.428571	0.0	0.953472	1.0	0.75	0.2	0.0	0.381229	1.0	0.657143	...	0.285714	0.0	0.0	0.250	0.166667	0.666667	0.250	0.388889	0.000000	0.529412
911	0.166667	0.5	0.355762	1.0	0.00	0.2	0.0	0.615385	1.0	0.614286	...	0.214286	0.0	0.0	0.025	0.666667	0.666667	0.025	0.000000	0.066667	0.000000

5 rows × 27 columns

Model Building

#Logistic Regression
from sklearn.linear_model import LogisticRegression
model=LogisticRegression()

model.fit(x_train,y_train)

LogisticRegression()

pred=model.predict(x_test)

pred

array(['No', 'No', 'No', 'No', 'Yes', 'No', 'No', 'No', 'No', 'No', 'No',
       'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No',
       'No', 'No', 'Yes', 'No', 'No', 'No', 'Yes', 'No', 'No', 'No', 'No',
       'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No',
       'No', 'No', 'No', 'No', 'No', 'Yes', 'No', 'No', 'No', 'No', 'No',
       'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No',
       'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No',
       'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No',
       'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No',
       'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No',
       'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No',
       'No', 'No', 'Yes', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No',
       'Yes', 'No', 'No', 'No', 'Yes', 'No', 'No', 'No', 'No', 'No', 'No',
       'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No',
       'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'Yes', 'No',
       'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No',
       'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No',
       'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No',
       'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No',
       'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No',
       'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No',
       'Yes', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No',
       'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No',
       'No', 'No', 'No', 'No', 'Yes', 'No', 'No', 'No', 'No', 'No', 'No',
       'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No',
       'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No',
       'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No'], dtype=object)

y_test

442      No
1091     No
981     Yes
785      No
1332    Yes
       ... 
1439     No
481      No
124     Yes
198      No
1229     No
Name: Attrition, Length: 294, dtype: object

df

	Age	Attrition	BusinessTravel	DailyRate	Department	DistanceFromHome	Education	EducationField	EmployeeCount	EmployeeNumber	...	RelationshipSatisfaction	StandardHours	StockOptionLevel	TotalWorkingYears	TrainingTimesLastYear	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsSinceLastPromotion	YearsWithCurrManager
0	41	Yes	Travel_Rarely	1102	Sales	1	2	Life Sciences	1	1	...	1	80	0	8	0	1	6	4	0	5
1	49	No	Travel_Frequently	279	Research & Development	8	1	Life Sciences	1	2	...	4	80	1	10	3	3	10	7	1	7
2	37	Yes	Travel_Rarely	1373	Research & Development	2	2	Other	1	4	...	2	80	0	7	3	3	0	0	0	0
3	33	No	Travel_Frequently	1392	Research & Development	3	4	Life Sciences	1	5	...	3	80	0	8	3	3	8	7	3	0
4	27	No	Travel_Rarely	591	Research & Development	2	1	Medical	1	7	...	4	80	1	6	3	3	2	2	2	2
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1465	36	No	Travel_Frequently	884	Research & Development	23	2	Medical	1	2061	...	3	80	1	17	3	3	5	2	0	3
1466	39	No	Travel_Rarely	613	Research & Development	6	1	Medical	1	2062	...	1	80	1	9	5	3	7	7	1	7
1467	27	No	Travel_Rarely	155	Research & Development	4	3	Life Sciences	1	2064	...	2	80	1	6	0	3	6	2	0	3
1468	49	No	Travel_Frequently	1023	Sales	2	3	Medical	1	2065	...	4	80	0	17	3	2	9	6	0	8
1469	34	No	Travel_Rarely	628	Research & Development	8	3	Medical	1	2068	...	1	80	0	6	3	4	4	3	1	2

1470 rows × 35 columns

model.predict(ms.transform([[41,2,1102,2,2,1,1,1,0,94,2,4,3,1,3,1,1,11,3,80,8,0,1,6,4,1001,67,]]))

array(['Yes'], dtype=object)

from sklearn.metrics import accuracy_score,classification_report,roc_auc_score,roc_curve
print(accuracy_score(y_test,pred))

0.8469387755102041

print(classification_report(y_test,pred))

              precision    recall  f1-score   support

          No       0.85      0.99      0.91       245
         Yes       0.70      0.14      0.24        49

    accuracy                           0.85       294
   macro avg       0.78      0.57      0.58       294
weighted avg       0.83      0.85      0.80       294

Decision Tree Classiffier

from sklearn.tree import DecisionTreeClassifier

dtc=DecisionTreeClassifier()
dtc.fit(x_train,y_train)

DecisionTreeClassifier()

y_pred=dtc.predict(x_test)

y_pred

array(['No', 'No', 'No', 'Yes', 'No', 'Yes', 'Yes', 'No', 'No', 'No',
       'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'Yes',
       'No', 'No', 'No', 'Yes', 'Yes', 'No', 'No', 'Yes', 'No', 'Yes',
       'No', 'Yes', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'Yes',
       'No', 'No', 'No', 'No', 'Yes', 'No', 'Yes', 'No', 'No', 'No', 'No',
       'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'Yes', 'No',
       'Yes', 'Yes', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No',
       'Yes', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No',
       'No', 'No', 'No', 'No', 'Yes', 'No', 'Yes', 'No', 'Yes', 'No',
       'No', 'Yes', 'No', 'No', 'No', 'No', 'Yes', 'No', 'No', 'No', 'No',
       'Yes', 'No', 'No', 'No', 'Yes', 'No', 'No', 'No', 'No', 'No', 'No',
       'No', 'No', 'No', 'No', 'No', 'Yes', 'No', 'Yes', 'No', 'No', 'No',
       'No', 'No', 'Yes', 'No', 'Yes', 'No', 'No', 'No', 'No', 'Yes',
       'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No',
       'No', 'Yes', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'Yes',
       'Yes', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No',
       'No', 'No', 'No', 'No', 'No', 'No', 'Yes', 'No', 'No', 'No', 'No',
       'No', 'Yes', 'No', 'No', 'No', 'No', 'No', 'Yes', 'No', 'No',
       'Yes', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'Yes', 'No',
       'Yes', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'Yes',
       'No', 'No', 'No', 'No', 'No', 'No', 'No', 'Yes', 'No', 'No', 'No',
       'No', 'Yes', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No',
       'Yes', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No',
       'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'Yes',
       'Yes', 'No', 'No', 'No', 'Yes', 'No', 'No', 'No', 'No', 'Yes',
       'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'Yes', 'No',
       'Yes', 'Yes', 'Yes', 'No', 'Yes', 'No', 'No', 'No', 'No', 'No',
       'No', 'No', 'No', 'No', 'No'], dtype=object)

y_test

442      No
1091     No
981     Yes
785      No
1332    Yes
       ... 
1439     No
481      No
124     Yes
198      No
1229     No
Name: Attrition, Length: 294, dtype: object

from sklearn.metrics import accuracy_score,classification_report

print(accuracy_score(y_test,y_pred))

0.7312925170068028

print(classification_report(y_test,y_pred))

              precision    recall  f1-score   support

          No       0.84      0.84      0.84       245
         Yes       0.20      0.20      0.20        49

    accuracy                           0.73       294
   macro avg       0.52      0.52      0.52       294
weighted avg       0.73      0.73      0.73       294

from sklearn.metrics import accuracy_score,classification_report,roc_auc_score,roc_curve
probability=dtc.predict_proba(x_test)[:,1]

probability

array([0., 0., 0., 1., 0., 1., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
       0., 0., 0., 1., 0., 0., 0., 1., 1., 0., 0., 1., 0., 1., 0., 1., 0.,
       0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 1., 0., 1., 0., 0.,
       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 1., 1., 0., 0.,
       0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
       0., 0., 0., 0., 0., 1., 0., 1., 0., 1., 0., 0., 1., 0., 0., 0., 0.,
       1., 0., 0., 0., 0., 1., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0.,
       0., 0., 0., 0., 1., 0., 1., 0., 0., 0., 0., 0., 1., 0., 1., 0., 0.,
       0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0.,
       0., 0., 0., 0., 0., 0., 1., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
       0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 1., 0., 0., 0.,
       0., 0., 1., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 1.,
       0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0.,
       1., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0.,
       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
       0., 0., 1., 1., 0., 0., 0., 1., 0., 0., 0., 0., 1., 0., 0., 0., 0.,
       0., 0., 0., 0., 0., 1., 0., 1., 1., 1., 0., 1., 0., 0., 0., 0., 0.,
       0., 0., 0., 0., 0.])

Hyper Parameter Tuning

from sklearn import tree
plt.figure(figsize=(25,15))
tree.plot_tree(dtc,filled=True)

[Text(417.8147194602273, 791.4176470588235, 'X[20] <= 0.038\ngini = 0.269\nsamples = 1176\nvalue = [988, 188]'),
 Text(77.14772727272728, 743.4529411764706, 'X[12] <= 0.75\ngini = 0.5\nsamples = 78\nvalue = [39, 39]'),
 Text(38.04545454545455, 695.4882352941177, 'X[2] <= 0.171\ngini = 0.426\nsamples = 39\nvalue = [27, 12]'),
 Text(29.590909090909093, 647.5235294117647, 'gini = 0.0\nsamples = 3\nvalue = [0, 3]'),
 Text(46.5, 647.5235294117647, 'X[14] <= 0.189\ngini = 0.375\nsamples = 36\nvalue = [27, 9]'),
 Text(25.363636363636367, 599.5588235294117, 'X[16] <= 0.5\ngini = 0.444\nsamples = 6\nvalue = [2, 4]'),
 Text(16.90909090909091, 551.5941176470587, 'X[9] <= 0.821\ngini = 0.444\nsamples = 3\nvalue = [2, 1]'),
 Text(8.454545454545455, 503.6294117647059, 'gini = 0.0\nsamples = 2\nvalue = [2, 0]'),
 Text(25.363636363636367, 503.6294117647059, 'gini = 0.0\nsamples = 1\nvalue = [0, 1]'),
 Text(33.81818181818182, 551.5941176470587, 'gini = 0.0\nsamples = 3\nvalue = [0, 3]'),
 Text(67.63636363636364, 599.5588235294117, 'X[9] <= 0.407\ngini = 0.278\nsamples = 30\nvalue = [25, 5]'),
 Text(50.727272727272734, 551.5941176470587, 'X[13] <= 0.071\ngini = 0.494\nsamples = 9\nvalue = [5, 4]'),
 Text(42.27272727272727, 503.6294117647059, 'gini = 0.0\nsamples = 5\nvalue = [5, 0]'),
 Text(59.18181818181819, 503.6294117647059, 'gini = 0.0\nsamples = 4\nvalue = [0, 4]'),
 Text(84.54545454545455, 551.5941176470587, 'X[22] <= 0.833\ngini = 0.091\nsamples = 21\nvalue = [20, 1]'),
 Text(76.0909090909091, 503.6294117647059, 'gini = 0.0\nsamples = 20\nvalue = [20, 0]'),
 Text(93.0, 503.6294117647059, 'gini = 0.0\nsamples = 1\nvalue = [0, 1]'),
 Text(116.25, 695.4882352941177, 'X[9] <= 0.364\ngini = 0.426\nsamples = 39\nvalue = [12, 27]'),
 Text(93.0, 647.5235294117647, 'X[0] <= 0.369\ngini = 0.133\nsamples = 14\nvalue = [1, 13]'),
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from sklearn.model_selection import GridSearchCV
parameter={
 'criterion':['gini','entropy'],
  'splitter':['best','random'],
  'max_depth':[1,2,3,4,5],
  'max_features':['auto', 'sqrt', 'log2']

}

grid_search=GridSearchCV(estimator=dtc,param_grid=parameter,cv=5,scoring="accuracy")

grid_search.fit(x_train,y_train)

GridSearchCV(cv=5, estimator=DecisionTreeClassifier(),
             param_grid={'criterion': ['gini', 'entropy'],
                         'max_depth': [1, 2, 3, 4, 5],
                         'max_features': ['auto', 'sqrt', 'log2'],
                         'splitter': ['best', 'random']},
             scoring='accuracy')

grid_search.best_params_

{'criterion': 'entropy',
 'max_depth': 3,
 'max_features': 'auto',
 'splitter': 'random'}

dtc_cv=DecisionTreeClassifier(criterion= 'entropy',
 max_depth=3,
 max_features='sqrt',
 splitter='best')
dtc_cv.fit(x_train,y_train)

DecisionTreeClassifier(criterion='entropy', max_depth=3, max_features='sqrt')

pred=dtc_cv.predict(x_test)

print(classification_report(y_test,pred))

              precision    recall  f1-score   support

          No       0.83      1.00      0.91       245
         Yes       0.00      0.00      0.00        49

    accuracy                           0.83       294
   macro avg       0.42      0.50      0.45       294
weighted avg       0.69      0.83      0.76       294

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\metrics\_classification.py:1248: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\metrics\_classification.py:1248: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\metrics\_classification.py:1248: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))

Random Forest

from sklearn.ensemble import RandomForestClassifier
rfc=RandomForestClassifier()
9rfc.fit(x_train,y_train)

RandomForestClassifier()

y2_pred=rfc.predict(x_test)
y2_pred

array(['No', 'No', 'No', 'No', 'Yes', 'No', 'No', 'No', 'No', 'No', 'No',
       'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'Yes', 'No',
       'No', 'No', 'No', 'No', 'No', 'No', 'Yes', 'No', 'No', 'No', 'No',
       'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No',
       'No', 'No', 'No', 'No', 'Yes', 'No', 'No', 'No', 'No', 'No', 'No',
       'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No',
       'No', 'No', 'No', 'No', 'No', 'Yes', 'No', 'No', 'No', 'No', 'No',
       'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No',
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       'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No',
       'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No',
       'No', 'No', 'No', 'No', 'Yes', 'No', 'No', 'No', 'No', 'No', 'No',
       'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No',
       'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No',
       'No', 'No', 'No', 'No', 'No', 'No', 'No', 'No'], dtype=object)

y_test

442      No
1091     No
981     Yes
785      No
1332    Yes
       ... 
1439     No
481      No
124     Yes
198      No
1229     No
Name: Attrition, Length: 294, dtype: object

print(accuracy_score(y_test,y2_pred))

0.8333333333333334

print(classification_report(y_test,y2_pred))

              precision    recall  f1-score   support

          No       0.85      0.98      0.91       245
         Yes       0.50      0.12      0.20        49

    accuracy                           0.83       294
   macro avg       0.67      0.55      0.55       294
weighted avg       0.79      0.83      0.79       294

forest_params = [{'max_depth': list(range(10, 15)), 'max_features': list(range(0,14))}]

rfc_cv= GridSearchCV(rfc,param_grid=forest_params,cv=10,scoring="accuracy")

rfc_cv.fit(x_train,y_train)

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 1043, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 861, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 779, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 903, in fit
    super().fit(
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 289, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 1043, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 861, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 779, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 903, in fit
    super().fit(
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 289, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 1043, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 861, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 779, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 903, in fit
    super().fit(
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 289, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 1043, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 861, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 779, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 903, in fit
    super().fit(
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 289, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 1043, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 861, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 779, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 903, in fit
    super().fit(
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 289, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 1043, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 861, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 779, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 903, in fit
    super().fit(
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 289, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 1043, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 861, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 779, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 903, in fit
    super().fit(
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 289, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 1043, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 861, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 779, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 903, in fit
    super().fit(
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 289, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 1043, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 861, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 779, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 903, in fit
    super().fit(
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 289, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 1043, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 861, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 779, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 903, in fit
    super().fit(
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 289, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 1043, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 861, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 779, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 903, in fit
    super().fit(
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 289, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 1043, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 861, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 779, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 903, in fit
    super().fit(
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 289, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 1043, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 861, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 779, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 903, in fit
    super().fit(
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 289, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 1043, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 861, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 779, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 903, in fit
    super().fit(
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 289, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 1043, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 861, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 779, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 903, in fit
    super().fit(
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 289, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 1043, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 861, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 779, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 903, in fit
    super().fit(
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 289, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 1043, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 861, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 779, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 903, in fit
    super().fit(
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 289, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 1043, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 861, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 779, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 903, in fit
    super().fit(
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 289, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 1043, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 861, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 779, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 903, in fit
    super().fit(
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 289, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 1043, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 861, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 779, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 903, in fit
    super().fit(
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 289, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 1043, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 861, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 779, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 903, in fit
    super().fit(
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 289, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 1043, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 861, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 779, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 903, in fit
    super().fit(
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 289, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 1043, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 861, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 779, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 903, in fit
    super().fit(
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 289, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 1043, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 861, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 779, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 903, in fit
    super().fit(
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 289, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 1043, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 861, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 779, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 903, in fit
    super().fit(
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 289, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 1043, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 861, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 779, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 903, in fit
    super().fit(
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 289, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 1043, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 861, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 779, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 903, in fit
    super().fit(
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 289, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 1043, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 861, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 779, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 903, in fit
    super().fit(
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 289, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 1043, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 861, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 779, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 903, in fit
    super().fit(
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 289, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 1043, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 861, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 779, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 903, in fit
    super().fit(
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 289, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 1043, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 861, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 779, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 903, in fit
    super().fit(
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 289, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 1043, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 861, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 779, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 903, in fit
    super().fit(
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 289, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 1043, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 861, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 779, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 903, in fit
    super().fit(
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 289, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 1043, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 861, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 779, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 903, in fit
    super().fit(
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 289, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 1043, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 861, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 779, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 903, in fit
    super().fit(
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 289, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 1043, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 861, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 779, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 903, in fit
    super().fit(
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 289, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 1043, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 861, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 779, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 903, in fit
    super().fit(
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 289, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 1043, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 861, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 779, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 903, in fit
    super().fit(
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 289, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 1043, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 861, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 779, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 903, in fit
    super().fit(
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 289, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 1043, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 861, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 779, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 903, in fit
    super().fit(
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 289, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 1043, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 861, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 779, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 903, in fit
    super().fit(
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 289, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 1043, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 861, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 779, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 903, in fit
    super().fit(
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 289, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 1043, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 861, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 779, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 903, in fit
    super().fit(
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 289, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 1043, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 861, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 779, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 903, in fit
    super().fit(
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 289, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 1043, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 861, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 779, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 903, in fit
    super().fit(
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 289, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 1043, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 861, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 779, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 903, in fit
    super().fit(
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 289, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 1043, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 861, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 779, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 903, in fit
    super().fit(
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 289, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 1043, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 861, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 779, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 903, in fit
    super().fit(
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 289, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 1043, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 861, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 779, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 903, in fit
    super().fit(
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 289, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:615: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 598, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 387, in fit
    trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 1043, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 861, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 779, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\_forest.py", line 169, in _parallel_build_trees
    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 903, in fit
    super().fit(
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\tree\_classes.py", line 289, in fit
    raise ValueError("max_features must be in (0, n_features]")
ValueError: max_features must be in (0, n_features]

  warnings.warn("Estimator fit failed. The score on this train-test"
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_search.py:922: UserWarning: One or more of the test scores are non-finite: [       nan 0.84950022 0.85543966 0.85885122 0.85541069 0.85797479
 0.85969144 0.85543966 0.85457772 0.86142257 0.85715631 0.85034767
 0.86054614 0.85375199        nan 0.84948573 0.85714182 0.85457048
 0.85543966 0.85716355 0.86309576 0.85457772 0.85883674 0.85204259
 0.85798928 0.85289729 0.85034043 0.85544691        nan 0.85033319
 0.85798928 0.85800377 0.85628712 0.85801101 0.85969868 0.85628712
 0.85886571 0.85543966 0.85459945 0.85713458 0.85460669 0.85630161
        nan 0.84947849 0.85289005 0.85714907 0.86055338 0.85882949
 0.85459221 0.85458496 0.86054614 0.85631609 0.85204983 0.85628712
 0.85713458 0.85543966        nan 0.85203535 0.85373026 0.85542518
 0.85885847 0.85884398 0.85287556 0.85715631 0.85970592 0.85289005
 0.85969868 0.85459221 0.86311749 0.85799652]
  warnings.warn(

GridSearchCV(cv=10, estimator=RandomForestClassifier(),
             param_grid=[{'max_depth': [10, 11, 12, 13, 14],
                          'max_features': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,
                                           12, 13]}],
             scoring='accuracy')

y3_pred=rfc_cv.predict(x_test)
print(accuracy_score(y_test,y3_pred))

0.8367346938775511

print(classification_report(y_test,y3_pred))

              precision    recall  f1-score   support

          No       0.85      0.98      0.91       245
         Yes       0.54      0.14      0.23        49

    accuracy                           0.84       294
   macro avg       0.69      0.56      0.57       294
weighted avg       0.80      0.84      0.79       294

rfc_cv.best_params_

{'max_depth': 14, 'max_features': 12}